AutogenAI

Modules, Assurance And Customers

What A Bid Team Gets

• AutogenAI is an enterprise platform for writing proposals, bids and grant applications, covering opportunity qualification, drafting, research, review and compliance rather than just generating text.
• Custom language engines are the differentiator — built on each customer's own tone of voice and connected to their existing content and knowledge repositories.
• Six modules: Qualify and Extract, Manage, Write, Research, Review and Integrations.
• FedRAMP High authorisation alongside ISO 27001, SOC 2 Type II, GDPR and NIST RMF — an unusually complete stack that opens US federal work.
• More than 270 companies are cited as customers, including Serco, Fujitsu, Siemens, HGS and Specsavers.

Why Bid Writing Suits This Better Than Most Writing Does

Proposal work has a shape that makes it unusually well matched to automation, and it is worth setting out because it explains the whole category.

First, it is enormously repetitive at the component level. Any organisation that bids regularly answers the same questions repeatedly — your health and safety policy, your quality management approach, your approach to social value, your relevant experience. The specifics change; the substance rarely does.

Second, it is deadline-driven and lumpy. Tenders arrive with fixed submission dates, frequently several at once, and the work cannot be spread evenly. Bid teams are either idle or working weekends, which makes capacity the binding constraint rather than skill.

Third, it is compliance-heavy. Tenders specify word limits, mandatory sections, evidence requirements and formatting rules, and a non-compliant bid can be disqualified before anyone reads its quality. Checking that mechanically is exactly the sort of task humans do badly at 2am.

Fourth, and most importantly, the outcome is binary and the value is large. You win the contract or you do not. That makes the return on any marginal improvement very legible to a finance director, which is why enterprise pricing survives here.

Put together, that is a far stronger case than most AI writing applications have — stronger than volume SEO writers like Byword can make. The work genuinely is repetitive, the constraint genuinely is capacity, and the payoff genuinely is measurable.

Bid teams are either idle or working weekends. The binding constraint has always been capacity, not writing skill — which is exactly the constraint software can move.

Custom Language Engines Are The Actual Product

The phrase sounds like marketing and describes the thing that makes this different from drafting a proposal in a general chat model.

A bid does not just need to be well written. It needs to sound like your organisation, use your terminology, reflect your accreditations, cite your actual case studies and describe methodologies you genuinely operate. Generic excellent prose that describes a company you are not is worse than mediocre prose that is accurate.

Building an engine on a customer's own tone of voice, connected to their knowledge repository, is what closes that gap. It means the draft arrives already carrying your language and your evidence rather than requiring a rewrite to insert both.

Patent drafting platforms such as Solve Intelligence solve the same grounding problem in another regulated discipline. That also addresses the most dangerous failure mode in AI-assisted bidding, which is not poor writing — it is a confident, fluent claim your organisation cannot substantiate. A bid that asserts a certification you do not hold or an experience you do not have is a serious problem, not a typo. Grounding generation in your actual content library is the structural defence against that.

The Research module extends the same logic to evidence sourcing, and Review to compliance checking. Together they describe a product built by people who have sat in a bid room, rather than a writing tool with a tender template.

The honest limit is that none of it supplies strategy. Deciding which opportunities to pursue, what your win themes are, and how to price remain the parts that actually determine outcomes.

FedRAMP High Is The Detail That Matters Most

Buried in the certification list is the single most commercially significant fact about this company.

FedRAMP High authorisation is the US federal government's most stringent cloud security standard, applied to systems handling data where compromise would have severe or catastrophic effect. Achieving it is a long, expensive process involving hundreds of controls and continuous monitoring, and comparatively few companies of any size hold it.

The practical consequence is that federal contractors can use AutogenAI on the proposals they write for government agencies. Most AI writing tools cannot be used for that work at all, regardless of how good they are, because the security posture disqualifies them before capability is discussed.

That converts a certification into a market position. For a company bidding for US federal contracts, the shortlist of usable AI proposal tools is short, and this is on it.

General legal AI like Harvey competes on breadth rather than on this authorisation. The rest of the stack is genuinely strong too — ISO 27001, SOC 2 Type II, NIST RMF and GDPR with AICPA attestation covers UK, EU and enterprise procurement as well as US federal.

For any buyer, this is also the right lens on the sensitivity of the data. An unsubmitted bid contains your pricing approach, your win themes and your assessment of competitors. It is among the most commercially sensitive material an organisation produces, and a tool holding it needs exactly this kind of posture.

1. Take three bids you have already submitted — one won, one lost, one you ran out of time on.
2. Have the platform draft the same responses from your own content library and compare against what you sent.
3. Check specifically whether any generated claim is one you could not evidence — that is the real risk.
4. Test the Review module against a tender with strict word limits and mandatory sections.
5. Measure how many bids you could have submitted rather than how fast you wrote the ones you did.

Reading The Outcome Figures

Four numbers appear prominently and they need very different amounts of scepticism.

70% increase in drafting speed and 85% increase in productivity are plausible and are the least interesting. They describe doing the same work faster, which is what you would expect and which is measurable on your own baseline.

100% increase in bids submitted is the most meaningful figure of the four, and it is the one to focus on. In bid work, capacity is the constraint — a team that can only produce four submissions a quarter is declining opportunities it might have won. Doubling throughput changes the shape of the pipeline rather than only the effort per bid.

241% increase in success rates is the one to treat carefully. Win rates depend overwhelmingly on factors outside any writing tool: whether you were the incumbent, whether your price was competitive, whether you had the right past performance, whether the buyer had already decided. A drafting platform can improve the quality and compliance of a submission, and attributing a multiple of win rate to it stretches the causal chain a long way.

There is also a selection effect worth naming: organisations that adopt bid software are frequently the ones simultaneously professionalising their whole bid function. The tool arrives alongside better processes, and the improvement is shared.

None of these are dishonest. They are customer-reported, unbaselined figures, and the sensible response is to measure bids submitted on your own numbers during a pilot.

Enterprise Seats And A Sales Call

This site does not publish plan prices, and AutogenAI does not either. The structure is what you can plan around.

The model is enterprise, contact-sales, with seat-based licensing. There is no self-serve entry, which is consistent with a product built around a custom language engine trained on your content — that is an onboarding project, not a signup.

The variables that typically drive a quote here are the number of bid team seats, whether federal deployment is required, and how much content library integration the language engine build involves. That last one is where implementation effort lives and is worth scoping early.

For a bid function the arithmetic is unusually legible, which is why the category sustains enterprise pricing. If a platform lets you submit more bids and your average contract value is known, the calculation almost writes itself — and unlike most software, one additional win can cover several years of licence.

Two questions to put directly. Whether FedRAMP High deployment is a separate contract or environment, since federal-authorised instances usually are. And what happens to your trained language engine and content library if you leave — that is your institutional knowledge, and portability is worth settling before it accumulates.

Given no trial exists, the pilot itself is the evaluation. Insist on running it against real historical bids rather than a demo tender.